[ONGOING WORK]

Authors

Roland Bouillot - Université Catholique de Louvain & Maastricht University 

Bertrand Candelon - Université Catholique de Louvain 

Clemens Kool - Maastricht University 

Abstract

Financial fragmentation in the Euro Area has become a hot topic since the European Central Bank must decide whether to maintain its interest rates high and long enough to tame inflation or loosen its monetary policy to mitigate the risk of another European sovereign debt crisis. This study leverages a novel high-dimensional dataset covering a wide range of fields within 10 European countries over the 2007 to 2024 period. This Big Data dataset has been exploited by a new machine learning technique (XGBoost) to find evidence of the financial fragmentation risk in the Euro Area. Most importantly, our main result suggests that the predicted long-term yield spreads of peripheral countries rise while those in core countries’ rise remain contained or even decrease. This divergence in yield differentials put a lot of scrutiny on the action of the ECB and call for policy guidance to avoid a new European sovereign debt crisis.

Keywords: Machine Learning, Financial Fragmentation, XGBoost, Sovereign spreads 

[ONGOING WORK]

Authors

Roland Bouillot - Université Catholique de Louvain & Maastricht University 

Bertrand Candelon - Université Catholique de Louvain 

Clemens Kool - Maastricht University 

Abstract

This paper studies the financial fragmentation risk in the Euro area sovereign bond market using a combination of machine learning forecasting and network analysis. By applying the XGBoost model to predict sovereign bond yield spreads and constructing correlation-based networks, our analysis identifies structural patterns of financial fragmentation across different forecast horizons. The results indicate that core Euro area economies -such as Austria, Finland and the Netherlands- exhibit strong co-movements in their sovereign spreads, while peripheral countries -including Greece, Ireland and, at longer horizons, France- display weaker correlations. Network analysis highlights key intermediaries in sovereign risk transmission, with Italy and Greece showing strong linkages, while Ireland and Belgium appear to be disconnected. Additionally, we designed a fragmentation indicator, based on betweenness and closeness centrality, that quantifies the degree of financial segmentation over time. Despite policy efforts to enhance financial integration, our findings suggest that fragmentation remains a persistent feature of the Euro area sovereign bond market.

Keywords: Machine Learning, Financial Fragmentation, Network Centrality, Sovereign Spreads, Euro Area

[ONGOING WORK]

Authors

Roland Bouillot - Université Catholique de Louvain & Maastricht University 

Siavash Mohades - Maastricht University & London Business School

Keywords: Monetary Policy, Credit Structure, Financial Fragmentation, Euro Area, Bank Lending